Self-organization and collective computation capability are the distinctive advantages of neural nets as compared with the conventional signal processing. One of the most important neural net models is the associative memory. Among all the associative memory models, the Hopfield neural net model has been proven to be suitable for optical implementation. Architectures proposed for the optical implementation of the Hopfield model include vector matrix multiplier models. D. Psaltis and N. Farhat, "Optical Information Processing Based on an Associative-Memory Model for Neural Nets with Thresholding and Feedback," Opt. Lett. 10, 98 (1985); N. Farhat, D. Psaltis, A. Prata and E. Paek, "Optical Implementation of the Hopfield Model," Appl. Opt. 24, 1469 (1985). Other architectures include holographic correlator models. B. H. Soffer, G. H. Dunning, Y. Owechiko and E. Marom, "Associative Holographic Memory with Feedback Using Phase-conjugating Mirrors," Opt. Lett. 11, 118 (1986); E. G. Pack and D. Psaltis, "Optical Associative Memory Using Fourier Transform Holograms," Opt. Eng. 26, 433 (1987).
There are several basic limitations associated with the Hopfield associative memory model. In the Hopfield neural net, the weights of the interconnection matrix are formulated by summing up the outer product of each of the memory vectors. The stable states produced by the iterative multiplication, thresholding and feedback may represent only local minima of the energy landscape associated with the system. Thus, spurious states may be obtained as the result of the associative recall. B. L. Montgomery and B. V. K. Vijaya Kuman, "Evaluation of the Use of the Hopfield Neural Network Model as a Nearest-neighbor Algorithm," Appl. Opt. 25, 3759 (1986). The performance of the Hopfield model also starts to deteriorate as the number of stored memory images increase beyond the limit of M.ltoreq.0.15 N, where M is the number of the stored images and N is the number of neurons.
The Hopfield neural network model is generally suitable for auto-associative recall. To conduct a hetero-associative recall, the system has to be modified to have a bidirectional structure that allows forward and backward information flow for two-way associative search. C. C. Guest and R. Tekolste, "Design and Devices for Optical Bidirectional Associative Memories," Appl. Opt. 26, 5055 (1987). The present invention introduces an innovative concept of an optical associative memory. The system performs a noniterative nearest neighbor search based on the shortest Hamming distance measurements between an input and each of the stored images. This optical feedforward network produces no spurious state and is suitable for both auto-associative and hetero-associative recalls. Moreover, high system storage capacity can be obtained since this capacity is only limited to the space-bandwidth product of the spatial light modulators utilized in the associative memory.